How Watson Works

Ivan Herman recently offered some insight into how Watson actually works. Herman reports, “I was at Chris Welty’s keynote yesterday at the WWW2012 Conference. His talk was on Jeopardy/Watson and, although this is not the first time I heard/saw something on Watson, some things really became clear only at his keynote. Namely: what is really the central paradigm that made the question answering mechanism so successful in the case of Watson? Well… query answering in Watson is not some sort of a deterministic algorithm that turns a natural language question into a query into a huge set of data. This approach does not work.”

He continues, “Instead, a question is analyzed and, based on search in various set of data, a large set of possible answers is extracted. These ‘candidate’ answers are analyzed separately along a whole series of different dimensions (geographical or temporal dimensions, or, which I found the most interesting, putting back candidate answers into the original question and search that again against various sources of information to rank them again). The result is a vector of numerical values representing the results of the analysis along those different dimensions. That ‘vector’ is summed up into one final value using a weight values for each dimension. The weights themselves are obtained through a prior training process (in this case using a number of stored Jeopardy question/answers). Finally, the answer with the highest value (I presume over a certain threshold value) is returned.”